But most cities still don’t use their own administrative data to inform their decisions. In many cases, they lack the technical capacity and have only limited resources.

Our lab has worked with the city of Arlington, Virginia to strengthen their services, particularly through the demographic planning and fire and police departments. Our work, which we are now expanding across Virginia and Iowa, shows how cities across America can leverage the data they already have to improve residents’ lives.

Start with existing data

By overlooking their administrate data, local governments are missing out on innovative ways to address their most pressing problems. Data provide a valuable way to describe the nature of the problems, assess the likely impact of potential solutions and predict future outcomes.

Housing value information from local property data is an excellent proxy for diversity, as it represents the wealth of the household. Individual local property values provide more timely and detailed information than federal data sets.

We used these data to determine measures of diversity, down to the census block level or even smaller. We calculated diversity by computing the probability that two housing values selected at random from the geographic area differ on home value. The higher the score, the more diverse the home values are in the region.

This information provided city demographers with a new lens to explore available housing opportunities and relate this information back to younger families with children, whose income is often lower than other families. Higher economic diversity is an indicator of more opportunities for all. It also helped them plan for school age enrollments by age.

Using all data

Government decisions have even more impact when local officials can combine their own data with other easily accessible outside resources, such as social media and state or federal databases. These combined sources can provide a more holistic view of our neighborhoods.

We looked at what time incidents were reported and responded to. The patterns for response time in relation to distance from the call appeared similar across stations. However, by plugging the data into a statistical model and controlling for several other variables – including month, hour of day and call type – we found that response times were highest for calls for hazardous materials investigations, highway calls and wires-down incidents.

These estimates helped the fire chief better anticipate when these incidents might occur and decide where to place fire or medic units and when to have more firefighters available.

Scaling and sustaining such an approach requires a new and bold agenda. The universities we work at are land grant institutions, meaning that they’re part of a class of universities founded to provide the working class with a practical education directly relevant to their daily lives. One of our primary roles is to translate evidence-based research for the public.

We believe that land grant universities like ours should build upon their current roles and bring data in service of the public good. By partnering more closely with local governments, researchers can help communities that are data-driven to govern more intelligently.